Along with the development of the economic society, the continuous increase of social activities of people and particularly the promotion of an urbanization process, urban population density increases. Therefore, population density estimation has a broad application prospect and high research value.
At present, a population density estimation method is to acquire images for a certain time period by virtue of intelligent video monitoring equipment, analyze the acquired images to obtain multiple image features and establish a regression model by virtue of the obtained image features; and during the real-time monitoring of the intelligent video monitoring equipment, a current frame image is analyzed to obtain an individual feature of the current frame image as an input, the number of people is calculated by virtue of the regression model, and then population density is calculated by virtue of the number of the people.
However, in the population density estimation method, when the regression model is established and the number of the people is calculated, there are many image features to be extracted, which may increase complexity in calculation and cause influence on a calculation speed; and moreover, during intelligent video monitoring, angle influence on an erection position of the monitoring equipment is not taken into account, so that the established regression model is not so accurate when the extracted image features are directly used for analysis, which further causes inaccuracy of the number of the people calculated by virtue of the regression model.
It can be seen that the population density estimation method in an existing technology is lower in calculation speed, and moreover, calculation results are inaccurate.